• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.05a
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• pp.1086-1092
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• 2002

Computer simulation is essential to design the suspension elements of railway vehicle. By computer simulation, engineers can assess the feasibility of a given design factors and change them to get a better design. But if one wishes to perform complex analysis on the simulation, such as railway vehicle dynamic, the computational time can become overwhelming. Therefore, many researchers have used a mega model that has a regression model made by sampling data through simulation. In this paper, the neural network is used a mega model that have twenty-nine design variables and forty-six responses. After this mega model is constructed, multi-objective optimal solutions are achieved by using the differential evolution. This paper shows that this optimization method using the neural network and the differential evolution is a very efficient tool to solve the complex optimization problem.

• International Journal of Aeronautical and Space Sciences
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• v.9 no.1
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• pp.100-110
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• 2008

It is not easy to monitor and identify all engine faults and conditions using conventional fault detection approaches like the GPA (Gas Path Analysis) method due to the nature and complexity of the faults. This study therefore focuses on a model based diagnostic method using Neural Network algorithms proposed for fault detection on a turbo shaft engine (PW 206C) selected as the power plant for a tilt rotor type unmanned aerial vehicle (Smart UAV). The model based diagnosis should be performed by a precise performance model. However component maps for the performance model were not provided by the engine manufacturer. Therefore they were generated by a new component map generation method, namely hybrid method using system identification and genetic algorithms that identifies inversely component characteristics from limited performance deck data provided by the engine manufacturer. Performance simulations at different operating conditions were performed on the PW206C turbo shaft engine using SIMULINK. In order to train the proposed BPNN (Back Propagation Neural Network), performance data sets obtained from performance analysis results using various implanted component degradations were used. The trained NN system could reasonably detect the faulted components including the fault pattern and quantity of the study engine at various operating conditions.

• Asian Journal of Atmospheric Environment
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• v.2 no.1
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• pp.60-67
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• 2008

The spatial limitations of urban environments in general lead to invention and design of a wide range of underground transportation systems such as subways, underground roads and paths, etc. Among them, the application of subway systems in metropolitan cities is most commonly observed to ease those confronted difficulties on this purpose. It in turn leaves passengers and workers to be exposed to indoor air potentially polluted by various sources existing in this underground environment. Specifically when considering the IAQ in a subway station, there exist many IAQ-related parameters to be counted either as individual or as integrated exposures. In this study, a model system has been developed to manage the general IAQ in a subway station. Field survey and $CO_2$ measurements were initially conducted to analyze and understand the relationship between the indoor and outdoor air quality while considering the internal pollution sources such as passengers, subway trains, etc. The measurement data were then employed for the model development with other static information. For the model development, the algorithm of simple continuity was built and applied to model the subway IAQ concerned. In this paper, the recent updated draft version of model developed will be reported and demonstrated.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.5
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• pp.1021-1030
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• 2021

This paper proposes S-CAFG(Stage-based Cyber Attack Flow Graph), a model for effectively describing training scenarios that simulate modern complex cyber attacks. On top of existing graph and tree models, we add a stage node to model more complex scenarios. In order to evaluate the proposed model, we create a complicated scenario and compare how the previous models and S-CAFG express the scenario. As a result, we confirm that S-CAFG can effectively describe various attack scenarios such as simultaneous attacks, additional attacks, and bypass path selection.

• Computers and Concrete
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• v.27 no.5
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• pp.489-512
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• 2021

In this study, two powerful techniques, namely particle swarm optimization (PSO) and imperialist competitive algorithm (ICA) were selected and combined with a pre-developed ANN model aiming at improving its performance prediction of the compressive strength of concrete modified with fly ash. To achieve this study's aims, a comprehensive database with 379 data samples was collected from the available literature. The output of the database is the compressive strength (CS) of concrete samples, which are influenced by 9 parameters as model inputs, namely those related to mix composition. The modeling steps related to ICA-ANN (or neuro-imperialism) and PSO-ANN (or neuro-swarm) were conducted through the use of several parametric studies to design the most influential parameters on these hybrid models. A comparison of the CS values predicted by hybrid intelligence techniques with the experimental CS values confirmed that the neuro-swarm model could provide a higher degree of accuracy than another proposed hybrid model (i.e., neuro-imperialism). The train and test correlation coefficient values of (0.9042 and 0.9137) and (0.8383 and 0.8777) for neuro-swarm and neuro-imperialism models, respectively revealed that although both techniques are capable enough in prediction tasks, the developed neuro-swarm model can be considered as a better alternative technique in mapping the concrete strength behavior.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.1
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• pp.147-165
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• 2021

In this work, we empirically evaluated the efficiency of the recent EfficientNetB0 model to identify and diagnose malaria parasite infections in blood smears. The dataset used was collected and classified by relevant experts from the Lister Hill National Centre for Biomedical Communications (LHNCBC). We prepared our samples with minimal image transformations as opposed to others, as we focused more on the feature extraction capability of the EfficientNetB0 baseline model. We applied transfer learning to increase the initial feature sets and reduced the training time to train our model. We then fine-tuned it to work with our proposed layers and re-trained the entire model to learn from our prepared dataset. The highest overall accuracy attained from our evaluated results was 94.70% from fifty epochs and followed by 94.68% within just ten. Additional visualization and analysis using the Gradient-weighted Class Activation Mapping (Grad-CAM) algorithm visualized how effectively our fine-tuned EfficientNetB0 detected infections better than other recent state-of-the-art DCNN models. This study, therefore, concludes that when fine-tuned, the recent EfficientNetB0 will generate highly accurate deep learning solutions for the identification of malaria parasites in blood smears without the need for stringent pre-processing, optimization, or data augmentation of images.

• Structural Engineering and Mechanics
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• v.77 no.5
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• pp.627-635
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• 2021

The speed of rail vehicles become higher and higher over two decades, and China has unveiled a prototype high-speed train in October 2020 that has been able to reach 400 km/h. At such high speeds, wheel-rail force items that had previously been ignored in common computational model should be reevaluated and reconsidered. Aiming at this problem, a new model for investigating the vehicle-bridge interaction at high moving speed is proposed. Comparing with the common model, the new model was more accurate and applicable, because it additionally considers the second-order pseudo-inertia forces effect and its modeling equilibrium position was based on the initial deformed curve of bridge, which could include the influences of temperature, pre-camber, shrinkage and creep deformation, and pier uneven settlement, etc. Taking 5 km/h as the speed interval, the dynamic responses of the classical vehicle-bridge system in the speed range of 5 km/h to 400 km/h are studied. The results show that ignoring the second-order pseudo-inertia force will underestimate the dynamic response of vehicle-bridge system and make the high-speed railway bridge structure design unsafe.

• Smart Media Journal
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• v.9 no.4
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• pp.17-25
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• 2020

In the image object classification problem, low-resolution images may have a negative impact on the classification result, especially when the classification method, such as a convolutional neural network (CNN) model, is trained on a high-resolution (HR) image dataset. In this paper, we analyze the behavior of applying a classical super-resolution (SR) method such as bicubic interpolation, and a deep CNN model such as SRCNN to enhance low-resolution (LR) weeds images used for classification. Using an HR dataset, we first train a CNN model for weeds image classification with a default input size of 128 × 128. Then, given an LR weeds image, we rescale to default input size by applying the bicubic interpolation or the SRCNN model. We analyze these two approaches on the Chonnam National University (CNU) weeds dataset and find that SRCNN is suitable for the image size is smaller than 80 × 80, while bicubic interpolation is convenient for a larger image.

• ETRI Journal
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• v.44 no.6
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• pp.1004-1019
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• 2022

For autonomous driving without high-definition maps, we present a model capable of generating multiple plausible paths from egocentric images for autonomous vehicles. Our generative model comprises two neural networks: feature extraction network (FEN) and path generation network (PGN). The FEN extracts meaningful features from an egocentric image, whereas the PGN generates multiple paths from the features, given a driving intention and speed. To ensure that the paths generated are plausible and consistent with the intention, we introduce an attentive discriminator and train it with the PGN under a generative adversarial network framework. Furthermore, we devise an interaction model between the positions in the paths and the intentions hidden in the positions and design a novel PGN architecture that reflects the interaction model for improving the accuracy and diversity of the generated paths. Finally, we introduce ETRIDriving, a dataset for autonomous driving, in which the recorded sensor data are labeled with discrete high-level driving actions, and demonstrate the state-of-the-art performance of the proposed model on ETRIDriving in terms of accuracy and diversity.

• Journal of the Korea Computer Graphics Society
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• v.29 no.1
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• pp.23-29
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• 2023

In this paper, we introduce a method to obtain high-quality results at a low cost for simulating musculoskeletal characters based on data from the reference motion through motion capture on two-legged walking through reinforcement learning. We reset the motion data of the reference motion to allow the character model to perform, and then train the corresponding motion to be learned through reinforcement learning. We combine motion imitation of the reference model with minimal metabolic energy for the muscles to learn to allow the musculoskeletal model to perform two-legged walking in the desired direction. In this way, the musculoskeletal model can learn at a lower cost than conventional manually designed controllers and perform high-quality bipedal walking.